 Algorithmic Design and Beowulf Cluster Implementation of Stochastic Simulation Code of Stochastic Simulation Code for Large Scale Non Linear ModelsGary Anderson And Raymond Board
No 128, Computing in Economics and Finance 2001 from Society for Computational Economics Abstract: Anderson & Moore describe a powerful method for solving linear saddle point models. The algorithm has proved useful in a wide array of applications including analyzing linear perfect foresight models, providing initial solutions and asymptotic constraints for nonlinear models. However, many algorithmic design choices remain in selecting components of a nonlinear certainty equivalence equation solver. This paper describes the present state of development of this set of tools. The paper descibes the results of simulation experiments using the FRBUS quarterly econometric model and the Canada Model. The paper provides data characterizing the impact of solution path length, initial path guess, terminal constraint strategy and strategies for exploiting sparsity on computation time, solution accuracy and memory requirements. The paper compares algorithm performance on traditional unix platform with our recent Beowulf Cluster Parallel Computation Implementation. Keywords: beowulf; parallel; stack algorithm; anderson-moore algortithm (search for similar items in EconPapers) There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf1:128 Access Statistics for this paper More papers in Computing in Economics and Finance 2001 from Society for Computational Economics Contact information at EDIRC. |
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